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English(EN) Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSD

核距离估计的Minimax下界被证明为 $n^{-1/2}$

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员已经确定，包括MMD、HSIC和KSD在内的核距离估计的Minimax下界在一般拓扑空间上为 $n^{-1/2}$。该速率在温和的核假设下实现，并证实了参数速率的最优性，即使在具有无界核的有限维欧几里得设置之外也是如此。这些发现也扩展到均值嵌入和中心交叉协方差算子的估计，解决了这些核距离最优估计的问题。 AI

影响 为机器学习中使用的关键分布比较方法建立了理论最优性。

排序理由 学术论文发表在arXiv上，详细介绍了机器学习领域的理论发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

核距离估计的Minimax下界被证明为 $n^{-1/2}$

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学术论文发表在arXiv上，详细介绍了机器学习领域的理论发现。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jose Cribeiro-Ramallo, Florian Kalinke, Zolt\'an Szab\'o ·

    核距离估计的最小最大下界：MMD, HSIC, KSD

    arXiv:2607.24235v1 Announce Type: new Abstract: Over the past 20 years, kernel discrepancies have been leveraged as a highly powerful tool for quantifying the disagreement of distributions, with numerous successful applications in two-sample, goodness-of-fit, and independence tes…

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